Modularization and Semantics of Learning Objects in a Cooperative Knowledge Space Nadine Ludwig Center for Multimedia in eLearning & eResearch, Berlin Inst. of Technol., Berlin; This paper appears in: Technology and Society, 2008. ISTAS 2008. IEEE International Symposium on Publication Date: 26-28 June 2008

Outline Introduction Defining Learning Objects in a Cooperative Knowledge Space Semantic Network of Learning Objects Modularization and Re-Combination of Learning Objects

1. Introduction most of these systems focus on providing content and do not support cooperative learning and work scenarios there are platforms that mainly concentrate on the user-centered and communicative aspects of learning for the most part disregarding standards-compliant content creation and modularization. This article will describe a potential solution to this problem by using the advantages of both types of platforms by merging and complementing their functions.

2. Defining Learning Objects in a Cooperative Knowledge Space The Learning Object Metadata (LOM) developed by the LTSC enables outsiders to see what the object represents without having to execute it. The LOM element set consists of nine metadata categories: 1. General Category 2. Lifecycle Category 3. Meta-Metadata 4. Technical Category 5. Educational Category 6. Rights Category 7. Relation Category 8. Annotation Category 9. Classification Category

For uploading laboratory applets, a special form is provided where the user can describe which devices and parameters are included and the applet file (*.jar) can then be uploaded

3. Semantic Network of Learning Objects The semantic network of the objects will be provided in two ways: • By behavior and position within the cooperative knowledge space • By contextual coherence On the one hand the objects will be observed regarding their position and behavior in the system. To store this information, the XML topic map standard in its 2nd version will be used.

The following topic types will be provided in the cooperative knowledge space: • Document (externally produced) • Content page (internally produced) • Binary object (pictures, videos etc.) • Laboratory (Java applet) • Room • User

We provide the following association types: To bring these topics into a semantic coherence, they have to be associated to each other. We provide the following association types: • User knows User (are “Buddies”) • User created/read Document • User implemented Laboratory • User owns key for Room • Document belongs to Laboratory • Content Page is linked to Content Page • Content Page is linked to Room The ontology is implemented using the ontology defining language OWL and is also stored in the relational database.

4. Modularization and Re-Combination of Learning Objects The topic map and the ontology help find relevant objects or give further information on a special topic or a lecture. In that way, learning objects can act like modules of different learning courses and can be re- combined in a new context as a SCORM course.